Analysing Gene Expression of Breast Cancer patients

Introduction

• 2 296 840 new breast cancer patients in 2022¹.

• Aim of project: Exploring and analyzing patterns in breast cancer gene expression data.

Materials and method

• The analysis was performed on the dataset “GDC TCGA Breast Cancer (BRCA)” from xenabrowser.net

• Our data:

  • Gene expression (RNAseq) and phenotype metadata

Notes: Materials: What data did you use and where did you get it from?

Methods: Data preparation

• Data obtained programatically

• Pivoted the gene expression dataset longer to be more tidy

• The two datasets were joined on the patient IDs

• Mutated the dataset to add new columns:

  • Age groups
  • Converted days to years for several relevant columns

• Analytical methods:

  • Descriptive data analysis, PCA and Linear Modelling

Notes: Methods: Which modelling did you use? Think of the methods section as a recipe for how to go from raw to results => Flow chart?

Methods:

Show flowchart here!!! (figure 1)

Descriptive analysis: Overview of the data

Descriptive analysis: Vitality

Descriptive analysis: Survival time and prior malignancy

Figure 6: Survival time by prior malignancy

Analysis: Linear model

Show Jonas plot here

Analysis: Investigating cancer stages

Analysis: PCA

Figure 9: Principal Component Analysis

Discussion:

• Catching the cancer in an early stage seems to increase chance of survival

• Limitations and future work

  • Compare against healthy tissue samples (eg. GTEX)